chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using Accord.Math;
+using Accord.Statistics;
+
+namespace QuantConnect.Algorithm.Framework.Portfolio
+{
+    /// <summary>
+    /// Provides an implementation of a portfolio optimizer with unconstrained mean variance.
+    /// </summary>
+    public class UnconstrainedMeanVariancePortfolioOptimizer : IPortfolioOptimizer
+    {
+        /// <summary>
+        /// Perform portfolio optimization for a provided matrix of historical returns and an array of expected returns
+        /// </summary>
+        /// <param name="historicalReturns">Matrix of historical returns where each column represents a security and each row returns for the given date/time (size: K x N).</param>
+        /// <param name="expectedReturns">Array of double with the portfolio annualized expected returns (size: K x 1).</param>
+        /// <param name="covariance">Multi-dimensional array of double with the portfolio covariance of annualized returns (size: K x K).</param>
+        /// <returns>Array of double with the portfolio weights (size: K x 1)</returns>
+        public double[] Optimize(double[,] historicalReturns, double[] expectedReturns = null, double[,] covariance = null)
+        {
+            var Π = (expectedReturns ?? historicalReturns.Mean(0));
+            var Σ = covariance ?? historicalReturns.Covariance();
+            return Π.Dot(Σ.Inverse());
+        }
+    }
+}